MotionSpaceFlow: Representation-Aware Flow Matching in Direct Motion Space

arXiv:2609.34190 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Transformer — 17× in this paper · explained in 2 episodes
• Fidelity — 13× in this paper · explained in 2 episodes
• Flow Matching — 12× in this paper · explained in 8 episodes
• Diffusion Model — 10× in this paper · explained in 8 episodes
• Alignment — 6× in this paper · explained in 1 episode
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Anisotropy — 3× in this paper · explained in 3 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 3 episodes
• Reconstruction Error — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Exposure Bias — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Joint Attention — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• 2-Wasserstein Distance — 1× in this paper · explained in 1 episode
• Absolute Motion — 1× in this paper · explained in 1 episode
• Anisotropic Data — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Diffusion Process — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Modality Gap — 1× in this paper · explained in 4 episodes
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
• UMO: Unified In-Context Learning Unlocks Motion Foundation Model Priors
• Classifier-Free Diffusion Guidance
• Absolute Coordinates Make Motion Generation Easy
• Kimodo: Scaling Controllable Human Motion Generation
• HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation

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